I think it's the other way round: More computer programmers have discovered Bayesian learning and similar probability-based techniques. They have been in use in information retrieval for decades, and Paul Graham popularized probabilty-based ideas only *after* these two researchers began working in that area.

An Erlang distribution is a Poisson distribution with an integer h parameter. It is that simple.

From: "An Intuitive Explanation of Bayesian Reasoning

Bayes' Theorem
for the curious and bewildered;
an excruciatingly gentle introduction.

By Eliezer Yudkowsky" link, here ... http://yudkowsky.net/bayes/bayes.html

Scrolling down a bit, one gets to ...

"Here's a story problem about a situation that doctors often encounter:

1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

What do you think the answer is? If you haven't encountered this kind of problem before, please take a moment to come up with your own answer before continuing."

The above isn't that intuitive!!!

The author shows how it can be made more intuitive, though, eg by re-phrasing the question and such ... Anyway, the link is well worth the read.

As an aside ... I wanted to reference the following ... http://www.amazon.com/gp/reader/0812975219/ref=sib_vae_pg_190/002-3703878-9607262?%5Fencoding=UTF8&keywords=side%20effect&p=S06C&twc=4&checkSum=pz%2F9ZdXCBpyqZdIfID9ZQOng7RgEisixKBBl9uInnR4%3D#reader-page but Amazon wouldn't let me cut and paste! (A case of how to lose friends and influence no one?).

Andrew Gelman has a critique of the Economist paper in his blog here ... http://www.stat.columbia.edu/~cook/movabletype/archives/2006/01/bayesian_parame.html#comments